Bioinformatics Advance Access originally published online on August 2, 2005
Bioinformatics 2005 21(19):3711-3718; doi:10.1093/bioinformatics/bti608
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Motif-based protein ranking by network propagation
1Department of Computer Science, Columbia University New York, NY 10027, USA
2Center for Computational Biology and Bioinformatics, Columbia University New York, NY 10027, USA
3Center for Computational Learning Systems, Columbia University New York, NY 10027, USA
4NEC Labs New Jersey, CA 95014, USA
5Department of Genome Sciences, University of Washington Seattle, WA 98195, USA
*To whom correspondence should be addressed.
Motivation: Sequence similarity often suggests evolutionary relationships between protein sequences that can be important for inferring similarity of structure or function. The most widely-used pairwise sequence comparison algorithms for homology detection, such as BLAST and PSI-BLAST, often fail to detect less conserved remotely-related targets.
Results: In this paper, we propose a new general graph-based propagation algorithm called MotifProp to detect more subtle similarity relationships than pairwise comparison methods. MotifProp is based on a protein-motif network, in which edges connect proteins and the k-mer based motif features that they contain. We show that our new motif-based propagation algorithm can improve the ranking results over a base algorithm, such as PSI-BLAST, that is used to initialize the ranking. Despite the complex structure of the protein-motif network, MotifProp can be easily interpreted using the top-ranked motifs and motif-rich regions induced by the propagation, both of which are helpful for discovering conserved structural components in remote homologies.
Availability: http://www.cs.columbia.edu/compbio/motifprop
Contact: cleslie{at}cs.columbia.edu
Received on May 30, 2005; revised on July 23, 2005; accepted on July 29, 2005
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